Binance's Strategy Factory Enhances Fraud Detection with AI

Binance Blog published a new article, introducing Binance's Strategy Factory, an AI-powered assistant designed to revolutionize rule-building in risk and fraud detection. Traditional methods of rule-building rely heavily on manual intuition and static logic, which often prove too slow and rigid to keep pace with evolving risks and complex fraud patterns. Strategy Factory addresses these challenges by automating and streamlining the creation and optimization of rules, enabling analysts to develop smarter, data-driven rules more efficiently. The article highlights the limitations of traditional rule-building, which typically involves a series of logic conditions known as Boolean expressions. These rules, while straightforward in theory, become increasingly complex in real-world scenarios where analysts must navigate vague business goals, vast datasets, and constantly changing user behaviors. Strategy Factory leverages AI to enhance human expertise, allowing analysts to make faster, more informed decisions. By automatically identifying effective combinations of features, thresholds, and signals based on real data, Strategy Factory adapts dynamically to changing behaviors over time, resulting in smarter rules, fewer false positives, and quicker responses to emerging risks. A key component of Strategy Factory is its use of decision trees, a common machine learning method for generating rules. However, decision trees often struggle with the messy, unpredictable nature of real-world fraud detection. To overcome this, Strategy Factory employs a method called Split, which constructs rules linearly, focusing on one condition at a time. This approach enhances interpretability and reduces computational overhead, making it ideal for large-scale detection and real-time monitoring. Additionally, Strategy Factory uses FIT, a module that generates a full set of complementary rules by looping through the Split process multiple times, ensuring comprehensive coverage of diverse patterns in complex datasets. The article also introduces Global Gain, a business-aware utility function that optimizes rule sets for real-world outcomes such as precision and recall. By embedding business logic into the optimization process, Global Gain ensures that each rule not only meets statistical thresholds but also delivers tangible operational impact. Furthermore, Strategy Factory incorporates Split Constraints, allowing analysts to inject domain knowledge and business requirements directly into the rule generation process. This ensures that each rule is operationally viable and aligned with business priorities. Strategy Factory has already become an integral tool across Binance's key risk domains, significantly reducing investigation times and improving rule coverage and precision. Looking ahead, Binance plans to expand Strategy Factory's capabilities by integrating conversational AI agents and large language models to further automate and optimize rule generation. These advancements aim to make intelligent rule generation effortless, transparent, and resilient, ultimately enhancing Binance's ability to protect its users through continuous innovation and optimization in risk detection.